diff --git a/Energy_prediction.ipynb b/Energy_prediction.ipynb
index 0eabc5ab938a88433c9ba14c496ddeec34d4e747..ca3f3b4091a03335f5baf5efcc57b21617146b34 100644
--- a/Energy_prediction.ipynb
+++ b/Energy_prediction.ipynb
@@ -4613,7 +4613,8 @@
    "id": "23b0ff00",
    "metadata": {},
    "source": [
-    "#  Q learning"
+    "#  Q learning\n",
+    "Refrenced from lab 10 ,11 "
    ]
   },
   {
@@ -4626,7 +4627,7 @@
     "# Filter data for a specific country \n",
     "country_data = df1[df1['Entity'] == 'United States'].sort_values(by='Year')\n",
     "\n",
-    "# Define the state as the year and the target as the renewable energy share\n",
+    "\n",
     "states = country_data['Year'].values\n",
     "targets = country_data['Renewable energy share in the total final energy consumption (%)'].values\n",
     "\n",
@@ -4657,16 +4658,16 @@
    "source": [
     "# Q-learning algorithm\n",
     "for episode in range(num_episodes):\n",
-    "    state = 0  # Start from the first year\n",
+    "    state = 0  \n",
     "    for t in range(len(states) - 1):\n",
     "        if np.random.uniform(0, 1) < epsilon:\n",
-    "            action = np.random.randint(len(states))  # Explore: select a random action\n",
+    "            action = np.random.randint(len(states))  \n",
     "        else:\n",
-    "            action = np.argmax(q_table[state])  # Exploit: select the action with max Q-value\n",
+    "            action = np.argmax(q_table[state])  \n",
     "\n",
     "        # Take action and observe the reward\n",
     "        next_state = state + 1 if action == state + 1 else state\n",
-    "        reward = -abs(targets[next_state] - targets[state])  # Reward is the negative absolute difference\n",
+    "        reward = -abs(targets[next_state] - targets[state])  \n",
     "\n",
     "        # Update Q-table\n",
     "        q_table[state, action] = q_table[state, action] + alpha * (\n",
@@ -4698,7 +4699,7 @@
     "for action in policy:\n",
     "    predicted_targets.append(targets[action])\n",
     "\n",
-    "# Calculate accuracy (here we use mean absolute error for simplicity)\n",
+    "# Calculate accuracy \n",
     "mae = np.mean(np.abs(np.array(predicted_targets) - targets))\n",
     "print(f\"Mean Absolute Error: {mae}\")"
    ]